Both wind and solar irradiance are considered as variable sources of energy. The generated power is dependent on varying weather conditions. In this study, three indicators are introduced: generation power-to-storage day ratio, photovoltaic-to-wind energy ratio, and reliability improvement indicator. The values of the indicators are determined for 5701 points located in Europe. The results have been presented on charts illustrating statistics of the indicators as well as on maps. This study illustrates various aspects of the solar and the wind energy potential in the context of energy storage. The results show that for the majority of locations, the cost of 1 kWh of storage must be up to 3.2 times less than the cost of 1 kW of a photovoltaic system. Also, it should be up to six times less than the unit cost of the wind turbine system at 50 m in order to decrease the system cost, depending on the number of autonomy days. For most of the locations, the nominal power of the photovoltaic system should be significantly lower than the power of the wind turbine if the system is to meet the required reliability. If the reliability of the power supply has to be increased from 95% to 98%, the nominal power of the photovoltaic generator has to be increased, depending on the assumed days of autonomy, between 1.25 and 1.45 times and the power of the wind turbine at 50 m between 1.3 and 2 times for the greater number of locations.
References
1.
J.
Contreras
, M.
Asensio
, P. M.
de Quevedo
et al, “Chapter 4: Demand response modeling
,” in Joint RES and Distribution Network Expansion Planning Under a Demand Response Framework
, edited by J.
Contreras
, M.
Asensio
, and P.
de Quevedo
(Academic Press
, 2016
), pp. 33
–40
.2.
Z.
Baum
, R. R.
Palatnik
, O.
Ayalon
et al, “Harnessing households to mitigate renewables intermittency in the smart grid
,” Renewable Energy
132
, 1216
–1229
(2019
).3.
A. R.
Jordehi
, “Optimisation of demand response in electric power systems, a review
,” Renewable Sustainable Energy Rev.
103
, 308
–319
(2019
).4.
J.
Kapica
, “Small scale stand-alone photovoltaic pumping system with brushless DC motor for irrigation in agriculture
,” J. Renewable Sustainable Energy
9
(6
), 063503
(2017
).5.
P.
Denholm
and T.
Mai
, “Timescales of energy storage needed for reducing renewable energy curtailment
,” Renewable Energy
130
, 388
–399
(2019
).6.
S.
Pullins
, “Why microgrids are becoming an important part of the energy infrastructure
,” Electr. J.
32
, 17
(2019
).7.
I.
Strnad
and R.
Prenc
, “Optimal sizing of renewable sources and energy storage in low-carbon microgrid nodes
,” Electr. Eng.
100
(3
), 1661
–1674
(2018
).8.
J. P.
Fossati
, A.
Galarza
, A.
Martín-Villate
et al, “A method for optimal sizing energy storage systems for microgrids
,” Renewable Energy
77
, 539
–549
(2015
).9.
A. S.
Jacob
, R.
Banerjee
, and P. C.
Ghosh
, “Sizing of hybrid energy storage system for a PV based microgrid through design space approach
,” Appl. Energy
212
, 640
–653
(2018
).10.
A. A.
Solomon
, D. M.
Kammen
, and D.
Callaway
, “Investigating the impact of wind–solar complementarities on energy storage requirement and the corresponding supply reliability criteria
,” Appl. Energy
168
, 130
–145
(2016
).11.
P. B. L.
Neto
, O. R.
Saavedra
, and D. Q.
Oliveira
, “The effect of complementarity between solar, wind and tidal energy in isolated hybrid microgrids
,” Renewable Energy
147
, 339
–355
(2020
).12.
Y. F.
Nassar
and S. Y.
Alsadi
“Assessment of solar energy potential in Gaza Strip-Palestine
,” Sustainable Energy Technol. Assess.
31
, 318
–328
(2019
).13.
R.
Prăvălie
, C.
Patriche
, and G.
Bandoc
, “Spatial assessment of solar energy potential at global scale. A geographical approach
,” J. Cleaner Prod.
209
, 692
–721
(2019
).14.
A.
Shahasvari
and M.
Akbari
, “Potential of solar energy in developing countries for reducing energy-related emissions
,” Renewable Sustainable Energy Rev.
90
, 275
–291
(2018
).15.
B. W.
Kariuki
and T.
Sato
, “Interannual and spatial variability of solar radiation energy potential in Kenya using Meteosat satellite
,” Renewable Energy
116
, 88
(2018
).16.
A.
Barragán-Escandón
, E.
Zalamea-León
, and J.
Terrados-Cepeda
, “Incidence of photovoltaics in cities based on indicators of occupancy and urban sustainability
,” Energies
12
(5
), 1
(2019
).17.
E.
Fillol
et al, “Spatiotemporal indicators of solar energy potential in the Guiana shield using GOES images
,” Renewable Energy
111
, 11
–25
(2017
).18.
J.
Bosch
, I.
Staffell
, and A. D.
Hawkes
, “Temporally explicit and spatially resolved global offshore wind energy potentials
,” Energy
163
, 766
–781
(2018
).19.
P.
Elsner
, “Continental-scale assessment of the African offshore wind energy potential: Spatial analysis of an under-appreciated renewable energy resource
,” Renewable Sustainable Energy Rev.
104
, 394
–407
(2019
).20.
P.
Enevoldsen
, F.-H.
Permien
, I.
Bakhtaoui
et al, “How much wind power potential does Europe have? Examining European wind power potential with an enhanced socio-technical atlas
,” Energy Policy
132
, 1092
–1100
(2019
).21.
B.
Wang
, L. D.
Cot
, L.
Adolphe
et al, “Cross indicator analysis between wind energy potential and urban morphology
,” Renewable Energy
113
, 989
–1006
(2017
).22.
G.
D'Amico
, F.
Petroni
, and F.
Prattico
, “Economic performance indicators of wind energy based on wind speed stochastic modeling
,” Appl. Energy
154
, 290
–297
(2015
).23.
S. H.
Siyal
, D.
Mentis
, and M.
Howells
, “Mapping key economic indicators of onshore wind energy in Sweden by using a geospatial methodology
,” Energy Convers. Manage.
128
, 211
–226
(2016
).24.
J.
Jurasz
and J.
Mikulik
, “Site selection for wind and solar parks based on resources temporal and spatial complementarity–mathematical modelling approach
,” Przegl. Elektrotechn.
1
(7
), 88
–93
(2017
).25.
T.
Alexis
, S.
Marc
, D.
Philippe
et al, “Impact of climate variability on optimal wind-solar energy mixes: The Italian case
,” arXiv:1812.09181 (2018
).26.
W.
Sun
and G. P.
Harrison
, “Wind-solar complementarity and effective use of distribution network capacity
,” Appl. Energy
247
, 89
–101
(2019
).27.
L.
Xu
, Z.
Wang
, and Y.
Liu
, “The spatial and temporal variation features of wind-sun complementarity in China
,” Energy Convers. Manage.
154
, 138
–148
(2017
).28.
H.
Zhang
et al, “Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data
,” Appl. Energy
216
, 172
–182
(2018
).29.
Y.
Tang
, J. W. M.
Cheng
, Q.
Duan
et al, “Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies
,” Renewable Energy
133
, 1099
–1107
(2019
).30.
F. J.
Rodríguez-Benítez
, C.
Arbizu-Barrena
, F. J.
Santos-Alamillos
et al, “Analysis of the intra-day solar resource variability in the Iberian Peninsula
,” Sol. Energy
171
, 374
–387
(2018
).31.
M.
Shahriari
and S.
Blumsack
, “Scaling of wind energy variability over space and time
,” Appl. Energy
195
, 572
–585
(2017
).32.
H.
Omrani
, P.
Drobinski
, T.
Arsouze
et al, “Spatial and temporal variability of wind energy resource and production over the North Western Mediterranean Sea: Sensitivity to air-sea interactions
,” Renewable Energy
101
, 680
–689
(2017
).33.
D. E.
Ighravwe
, “Techno-economic assessment of small-scale renewable energy storage technologies
,” Decis. Sci. Lett.
8
(3
), 363
–372
(2019
).34.
G. A. H.
Laugs
, R. M. J.
Benders
, and H. C.
Moll
, “Balancing responsibilities: Effects of growth of variable renewable energy, storage, and undue grid interaction
,” Energy Policy
139
, 111203
(2020
).35.
M.
Perez
, R.
Perez
, K. R.
Rábago
et al, “Overbuilding and curtailment: The cost-effective enablers of firm PV generation
,” Sol. Energy
180
, 412
–422
(2019
).36.
H.
Johlas
, S.
Witherby
, and J. R.
Doyle
, “Storage requirements for high grid penetration of wind and solar power for the MISO region of North America: A case study
,” Renewable Energy
146
, 1315
–1324
(2020
).37.
J.
Weniger
, T.
Tjaden
, and V.
Quaschning
, “Sizing of residential PV battery systems
,” Energy Procedia
46
, 78
–87
(2014
).38.
J. K.
Kaldellis
, D.
Zafirakis
, E. L.
Kaldelli
et al, “Cost benefit analysis of a photovoltaic-energy storage electrification solution for remote islands
,” Renewable Energy
34
(5
), 1299
–1311
(2009
).39.
R.
McKenna
, S.
Hollnaicher
, P. O.
vd Leye
et al, “Cost-potentials for large onshore wind turbines in Europe
,” Energy
83
, 217
–229
(2015
).40.
G.
Bandoc
, R.
Prăvălie
, C.
Patriche
et al, “Spatial assessment of wind power potential at global scale. A geographical approach
,” J. Cleaner Prod.
200
, 1065
–1086
(2018
).41.
C.
Budischak
, D.
Sewell
, H.
Thomson
et al, “Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time
,” J. Power Sources
225
, 60
–74
(2013
).42.
See https://power.larc.nasa.gov/ for “
NASA Power|Prediction of Worldwide Energy Resources
” (last accessed January 23, 2019
).43.
P. W.
Stackhouse
, T.
Zhang
, D.
Westberg
et al, Power release 8.0.1 (with GIS applications) methodology (data parameters, sources, and validation)
(NASA, NASA Langley Research center, 2018).44.
P.
Wais
, “A review of Weibull functions in wind sector
,” Renewable Sustainable Energy Rev.
70
, 1099
–1107
(2017
).45.
J.
Kapica
, H.
Pawlak
, and M.
Ścibisz
, “Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe
,” Agric. Syst.
139
, 238
–249
(2015
).46.
D. M.
Deaves
and I. G.
Lines
, “On the fitting of low mean windspeed data to the Weibull distribution
,” J. Wind Eng. Ind. Aerodyn.
66
(3
), 169
–178
(1997
).47.
M.
Dahbi
, A.
Benatiallah
, and M.
Sellam
, “The analysis of wind power potential in Sahara site of Algeria-an estimation using the ‘Weibull’ density function
,” Energy Procedia
36
, 179
–188
(2013
).48.
M. H.
Soulouknga
, S. Y.
Doka
, N.
Revanna
et al, “Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution
,” Renewable Energy
121
, 1
–8
(2018
).49.
J. A.
Duffie
and W. A.
Beckman
, Solar Engineering of Thermal Processes
, 4th ed. (John Wiley & Sons, Inc
., Hoboken, NJ, USA
, 2013
).50.
Y.
El Mghouchi
, A.
El Bouardi
, Z.
Choulli
et al, “New model to estimate and evaluate the solar radiation
,” Int. J. Sustainable Built Environ.
3
(2
), 225
–234
(2014
).51.
F.
Qianqian
, see https://www.mathworks.com/matlabcentral/fileexchange/33381-jsonlab-a-toolbox-to-encode-decode-json-files for “JSONLab: A Toolbox to Encode/Decode JSON Files, MATLAB Central File Exchange
” (last accessed January 16, 2019
).52.
See https://atb.nrel.gov/electricity/2019/ for “
NREL, 2019 Electricity ATB—2019 ATB, Annual Technology Baseline: Electricity
” (last accessed October 4, 2019
).53.
IRENA
, Electricity Storage and Renewables: Costs and Markets to 2030
(IRENA
, 2017
).54.
Y.
Li
, J.
Wang
, C.
Gu
et al, “Investment optimization of grid-scale energy storage for supporting different wind power utilization levels
,” J. Mod. Power Syst. Clean Energy
7
(6
), 1721
–1734
(2019
).55.
Y.
Liu
, X.
Wu
, J.
Du
et al, “Optimal sizing of a wind-energy storage system considering battery life
,” Renewable Energy
147
, 2470
–2483
(2020
).56.
G. A.
Barzegkar-Ntovom
, N. G.
Chatzigeorgiou
, A. I.
Nousdilis
et al, “Assessing the viability of battery energy storage systems coupled with photovoltaics under a pure self-consumption scheme
,” Renewable Energy
152
, 1302
–1309
(2020
).57.
S.
Sayago
, G.
Ovando
, J.
Almorox
et al, “Daily solar radiation from NASA-POWER product: Assessing its accuracy considering atmospheric transparency
,” Int. J. Remote Sens.
41
, 897
–814
(2020
).58.
K.
Oka
, W.
Mizutani
, and S.
Ashina
, “Climate change impacts on potential solar energy production: A study case in Fukushima, Japan
,” Renewable Energy
153
, 249
–260
(2020
).59.
P.
de Jong
, T. B.
Barreto
, C. A. S.
Tanajura
et al, “Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model
,” Renewable Energy
141
, 390
–401
(2019
).© 2020 Author(s).
2020
Author(s)
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